Instructions to use HIT-TMG/JevEmbed-Qwen3-Embedding-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use HIT-TMG/JevEmbed-Qwen3-Embedding-0.6B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("HIT-TMG/JevEmbed-Qwen3-Embedding-0.6B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
File size: 862 Bytes
5177213 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"model_name": "JevEmbed-Qwen3-Embedding-0.6B",
"base_model": "Qwen/Qwen3-Embedding-0.6B",
"base_model_sha256": "0437e45c94563b09e13cb7a64478fc406947a93cb34a7e05870fc8dcd48e23fd",
"adapter_sha256": "f75992447af7d137b81f253cfc3f7a59d2af0a585274b7b3fe127b3eb41f5546",
"merged_model_sha256": "d3e90fdeb21415b57f474d5ac24b9c9f557c538d54e28b651f8ce637c42f4032",
"format": "standalone Sentence Transformers model with merged LoRA weights",
"embedding_dimension": 1024,
"training_step": 3128,
"tested_prompt_count": 5,
"in_memory_merge": {
"max_absolute_difference": 6.891787052154541e-07,
"minimum_cosine": 1.0
},
"saved_model_reload": {
"max_absolute_difference": 6.891787052154541e-07,
"minimum_cosine": 1.0
},
"base_vs_lora_max_absolute_difference": 0.14514517784118652,
"versions": {
"torch": "2.8.0+cu129"
}
}
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